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Record W2022669003 · doi:10.5539/ass.v4n3p36

Approaches to Learning in Statistics

2009· article· en· W2022669003 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAsian Social Science · 2009
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsnot available
FundersMacquarie University
KeywordsPsychologySignificant differenceMathematics educationStatistics educationHigher educationWork (physics)Medical educationStatisticsPolitical scienceMedicine

Abstract

fetched live from OpenAlex

In the last decade, Australian higher education institutions have become a destination for many international students, mainly from South East Asia. If their educational culture is quite different to the Australian educational culture, then their approaches to learning might be different to that of local students. The aim of this pilot study was to investigate the approaches to learning of students in statistics units and relate these to background variables such as country of origin, gender and work commitments. This study is significant because there are many international students at Macquarie University and no data of this type has been collected for our students in the past.Our analyses showed that there were no significant differences in the approaches to learning of local and international; and male and female students; however, we found a significant difference between undergraduate and postgraduate students, with postgraduates more likely to adopt deep strategies to learning.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.596
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.456
GPT teacher head0.466
Teacher spread0.009 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it